Journal of Intelligent Computing Ye Qing 17 1 2026 https://doi.org/10.6025/jic/2026/17/1/1-13 https://www.dline.info/jic/fulltext/v17n1/jicv17n1_1.pdf This paper explores the application of data mining techniques to enhance the management and evaluation of distance education. It begins by contextualizing China's educational reforms, emphasizing decentralization and institutional autonomy, which have increased the need for data driven decision making. The study highlights the challenges of online learning remarkably low supervision, poor instructional quality, and high dropout rates and proposes Educational Data Mining (EDM) as a solution. EDM leverages algorithms like decision trees (especially C4.5), K-means, Apriori, SVM, KNN, and Naive Bayes to analyze student behavior, predict performance, and support timely interventions. Among these, the decision tree algorithm is selected for its interpretability, accuracy, and efficiency in handling diverse data types. The paper details the algorithm's computational framework, including information entropy and gain calculations, and presents an intelligent model for distance education management. Experimental results demonstrate that the optimized C4.5-based model improves both accuracy and processing speed compared to traditional methods. Association rule mining reveals significant behavioral patterns linked to student success, such as homework scores and forum participation. The study concludes that integrating data mining into distance education enables proactive, personalized support and more effective administrative oversight. While traditional assessment persists, algorithmic approaches offer a scalable, equitable pathway to enhance teaching, learning, and institutional management in the era of big data. Further refinement of these models is recommended for broader and more robust application.